English(EN) A cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding

新的AI算法用更少的标记数据预测焊接熔深 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-24 17:33

研究人员开发了新的焊接熔深状态预测算法，解决了传统监督深度学习方法的局限性。一种方法利用无监督域适应和渐进式源域扩展策略来提高模型在TIG和激光焊接等不同焊接过程中的性能。另一种方法采用自监督学习与物理信息神经网络和少样本学习相结合，以最少的标记数据实现激光焊接熔深预测的高精度。 AI

影响这些方法可以显著减少工业焊接应用中对大量标记数据的需求，为更高效、更自动化的质量控制铺平道路。

排序理由两篇arXiv论文详细介绍了用于焊接熔深预测的新型算法。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.AI TIER_1 English(EN) · Xinhua Tang · 2026-06-24 17:52

A cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding

Supervised deep learning has been widely used for weld penetration state classification; however, its performance often degrades significantly under domain shift, such as when transferring models between welding processes with distinct physical mechanisms:for instance, from arc-d…
arXiv cs.AI TIER_1 English(EN) · Haichao Cui · 2026-06-24 17:33

A welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks

The laser welding full-penetration is of critical importance, as it constitutes one of the fundamental factors in achieving defect-free welded joints. Accurate prediction of the penetration state is therefore essential for ensuring weld quality. To this end, this paper introduces…